Abnormal Temperatures, Climate Risk Disclosures and Bank Loan Pricing: International Evidence
Bibliographic record
Abstract
Abstract This paper examines the effect of abnormal‐temperature‐related climate risk on bank loan pricing. Using a sample of syndicated loans from 35 countries and jurisdictions, we find that banks charge higher interest rates for borrowers with higher climate risk. We also find that climate risk affects loan spreads of both long‐term and short‐term loans, and this effect is more pronounced for short‐term loans. Our cross‐sectional analyses reveal that voluntary climate risk disclosures in conference calls by borrowers mitigate the impact of climate risk on loan spreads, especially when lead banks have less climate‐risk‐related lending experience. In addition, the borrowing cost of high‐climate‐risk borrowers in the United States decreases after the SEC issued climate risk disclosure guidance. However, the ESG disclosure requirements in 19 other countries, which are not climate‐risk‐specific, do not alter the effect of climate risk on bank loan pricing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".